Climatic Research Unit

2008 Posters

Climate variability and changes in the climate extremes of Mexico as detected from the global to the local scaleJ. L. Vazquez-Aguirre and P.D. Jones

Analysis of daily instrumental data allowed the IPCC AR4 to reveal the global picture of observed changes in the extremes of temperature and precipitation. Further studies over North America (Canada, the United States and Mexico), focused in the percentile-based indices, and showed consistent trends with a warming of the climate system. Both assessments used the indices approach proposed by the WMO/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices. Here, the same indices were calculated for as many locations as possible in eastern Mexico. The comparison of changes in climate extremes as detected from the global to the local scale suggests that some of the local changes are in phase with the largescale signal. However, exploratory analysis of the natural variability show that lowfrequency oscillations and inter-annual variability will also play a major role in the modulation of the extremes (i.e., the first three principal components of summer rainfall in the region were found to be well correlated to the global temperature record, the Atlantic multi-decadal oscillation and the Niño 3.4 index). Since both, changes in the extremes and natural climate variations are expected to have increased impacts in the region, adaptation and decision-making strategies will require climate diagnostics as specific packages of user-oriented information.

A cooperative international project to image historical ship logbooks and related marine data and metadata, and digitize the meteorological and oceanographic observations for merger into the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and for utilization for climate research.

We assess the ability of the CMIP3 models used for the 4th IPCC assessment report to simulate the climate impacts of Southern Annular Mode (SAM). The models simulate realistic spatial patterns of surface air temperature (T) and precipitation response to the SAM, but the magnitudes of the T response is underestimated. The simulated patterns of SST and sea ice response are less realistic. The quality of simulation varies strongly between models. There is some correlation between model skill in simulating responses in different parameters, with models good at simulating one parameter also being good at simulating others. The underestimation of the temperature response to the SAM means that the models likely underestimate the temperature response to future changes in the SAM. In boreal summer this is likely to lead to an underestimation of the warming over the Antarctic continent associated with ozone recovery.